This notebook is a template with each step that you need to complete for the project.
Please fill in your code where there are explicit ? markers in the notebook. You are welcome to add more cells and code as you see fit.
Once you have completed all the code implementations, please export your notebook as a HTML file so the reviews can view your code. Make sure you have all outputs correctly outputted.
File-> Export Notebook As... -> Export Notebook as HTML
There is a writeup to complete as well after all code implememtation is done. Please answer all questions and attach the necessary tables and charts. You can complete the writeup in either markdown or PDF.
Completing the code template and writeup template will cover all of the rubric points for this project.
The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this notebook and also discuss the results in the writeup file.
Below is example of steps to get the API username and key. Each student will have their own username and key.
kaggle.json and use the username and key.
ml.t3.medium instance (2 vCPU + 4 GiB)Python 3 (MXNet 1.8 Python 3.7 CPU Optimized)!pip install -U pip
!pip install -U setuptools wheel
!pip install -U "mxnet<2.0.0" bokeh==2.0.1
!pip install autogluon --no-cache-dir
# Without --no-cache-dir, smaller aws instances may have trouble installing
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Attempting uninstall: pip
Found existing installation: pip 21.3.1
Uninstalling pip-21.3.1:
Successfully uninstalled pip-21.3.1
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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Collecting mxnet<2.0.0
Using cached mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl (49.1 MB)
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Found existing installation: bokeh 2.4.2
Uninstalling bokeh-2.4.2:
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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
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Building wheels for collected packages: fairscale, antlr4-python3-runtime, seqeval, future
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Successfully built fairscale antlr4-python3-runtime seqeval future
Installing collected packages: typish, tokenizers, text-unidecode, tensorboard-plugin-wit, sortedcontainers, sentencepiece, py4j, msgpack, heapdict, distlib, cymem, antlr4-python3-runtime, zict, yacs, xxhash, wrapt, typing-extensions, tqdm, toolz, tensorboard-data-server, tblib, spacy-loggers, spacy-legacy, smart-open, regex, pyrsistent, pyDeprecate, pyasn1-modules, Pillow, ordered-set, omegaconf, oauthlib, numpy, murmurhash, multidict, mdurl, locket, langcodes, importlib-resources, grpcio, future, frozenlist, filelock, fastprogress, defusedxml, charset-normalizer, cachetools, autocfg, asynctest, absl-py, yarl, wasabi, torch, tifffile, tensorboardX, scipy, responses, requests-oauthlib, PyWavelets, pydantic, pyarrow, preshed, platformdirs, patsy, partd, opencv-python-headless, nptyping, markdown-it-py, importlib-metadata, google-auth, fastcore, deprecated, catalogue, blis, async-timeout, aiosignal, xgboost, virtualenv, torchvision, torchtext, torchmetrics, statsmodels, srsly, scikit-image, rich, nlpaug, markdown, jsonschema, hyperopt, huggingface-hub, google-auth-oauthlib, gluonts, fastdownload, fairscale, dask, click, aiohttp, accelerate, typer, transformers, timm, tensorboard, sktime, seqeval, ray, qudida, pytorch-metric-learning, pmdarima, nltk, model-index, lightgbm, gluoncv, distributed, confection, catboost, thinc, tbats, pytorch-lightning, pathy, openmim, datasets, autogluon.common, albumentations, spacy, evaluate, autogluon.features, autogluon.core, fastai, autogluon.tabular, autogluon.multimodal, autogluon.vision, autogluon.timeseries, autogluon.text, autogluon
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Successfully installed Pillow-9.4.0 PyWavelets-1.3.0 absl-py-1.4.0 accelerate-0.13.2 aiohttp-3.8.4 aiosignal-1.3.1 albumentations-1.1.0 antlr4-python3-runtime-4.8 async-timeout-4.0.2 asynctest-0.13.0 autocfg-0.0.8 autogluon-0.6.2 autogluon.common-0.6.2 autogluon.core-0.6.2 autogluon.features-0.6.2 autogluon.multimodal-0.6.2 autogluon.tabular-0.6.2 autogluon.text-0.6.2 autogluon.timeseries-0.6.2 autogluon.vision-0.6.2 blis-0.7.9 cachetools-5.3.1 catalogue-2.0.8 catboost-1.1.1 charset-normalizer-3.1.0 click-8.0.4 confection-0.0.4 cymem-2.0.7 dask-2021.11.2 datasets-2.12.0 defusedxml-0.7.1 deprecated-1.2.14 distlib-0.3.6 distributed-2021.11.2 evaluate-0.3.0 fairscale-0.4.6 fastai-2.7.12 fastcore-1.5.29 fastdownload-0.0.7 fastprogress-1.0.3 filelock-3.12.1 frozenlist-1.3.3 future-0.18.3 gluoncv-0.10.5.post0 gluonts-0.11.12 google-auth-2.19.1 google-auth-oauthlib-0.4.6 grpcio-1.43.0 heapdict-1.0.1 huggingface-hub-0.15.1 hyperopt-0.2.7 importlib-metadata-6.6.0 importlib-resources-5.12.0 jsonschema-4.8.0 langcodes-3.3.0 lightgbm-3.3.5 locket-1.0.0 markdown-3.4.3 markdown-it-py-2.2.0 mdurl-0.1.2 model-index-0.1.11 msgpack-1.0.5 multidict-6.0.4 murmurhash-1.0.9 nlpaug-1.1.10 nltk-3.8.1 nptyping-1.4.4 numpy-1.21.6 oauthlib-3.2.2 omegaconf-2.1.2 opencv-python-headless-4.7.0.72 openmim-0.2.1 ordered-set-4.1.0 partd-1.4.0 pathy-0.10.1 patsy-0.5.3 platformdirs-3.1.1 pmdarima-1.8.5 preshed-3.0.8 py4j-0.10.9.7 pyDeprecate-0.3.2 pyarrow-12.0.0 pyasn1-modules-0.3.0 pydantic-1.10.9 pyrsistent-0.19.3 pytorch-lightning-1.7.7 pytorch-metric-learning-1.3.2 qudida-0.0.4 ray-2.0.1 regex-2023.6.3 requests-oauthlib-1.3.1 responses-0.18.0 rich-13.4.1 scikit-image-0.19.3 scipy-1.7.3 sentencepiece-0.1.99 seqeval-1.2.2 sktime-0.13.4 smart-open-5.2.1 sortedcontainers-2.4.0 spacy-3.5.3 spacy-legacy-3.0.12 spacy-loggers-1.0.4 srsly-2.4.6 statsmodels-0.13.5 tbats-1.1.3 tblib-1.7.0 tensorboard-2.11.2 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorboardX-2.6 text-unidecode-1.3 thinc-8.1.10 tifffile-2021.11.2 timm-0.6.13 tokenizers-0.13.3 toolz-0.12.0 torch-1.12.1 torchmetrics-0.8.2 torchtext-0.13.1 torchvision-0.13.1 tqdm-4.65.0 transformers-4.23.1 typer-0.7.0 typing-extensions-4.4.0 typish-1.9.3 virtualenv-20.21.1 wasabi-1.1.2 wrapt-1.15.0 xgboost-1.6.2 xxhash-3.2.0 yacs-0.1.8 yarl-1.9.2 zict-2.2.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
# create the .kaggle directory and an empty kaggle.json file
!mkdir -p /root/.kaggle
!touch /root/.kaggle/kaggle.json
!chmod 600 /root/.kaggle/kaggle.json
# Fill in your user name and key from creating the kaggle account and API token file
import json
kaggle_username = "lionelchong"
kaggle_key = "Key" # Key is stored in an .env variable
# Save API token the kaggle.json file
with open("/root/.kaggle/kaggle.json", "w") as f:
f.write(json.dumps({"username": kaggle_username, "key": kaggle_key}))
!pip install kaggle
# Download the dataset, it will be in a .zip file so you'll need to unzip it as well.
!kaggle competitions download -c bike-sharing-demand
# If you already downloaded it you can use the -o command to overwrite the file
!unzip -o bike-sharing-demand.zip
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import pandas as pd
from autogluon.tabular import TabularPredictor
# Create the train dataset in pandas by reading the csv
# Set the parsing of the datetime column so you can use some of the `dt` features in pandas later
train = pd.read_csv('train.csv')
train.head()
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0 | 3 | 13 | 16 |
| 1 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 8 | 32 | 40 |
| 2 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 |
| 3 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 3 | 10 | 13 |
| 4 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 0 | 1 | 1 |
# Simple output of the train dataset to view some of the min/max/varition of the dataset features.
# Create the test pandas dataframe in pandas by reading the csv, remember to parse the datetime!
test = pd.read_csv('test.csv')
test.head()
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-20 00:00:00 | 1 | 0 | 1 | 1 | 10.66 | 11.365 | 56 | 26.0027 |
| 1 | 2011-01-20 01:00:00 | 1 | 0 | 1 | 1 | 10.66 | 13.635 | 56 | 0.0000 |
| 2 | 2011-01-20 02:00:00 | 1 | 0 | 1 | 1 | 10.66 | 13.635 | 56 | 0.0000 |
| 3 | 2011-01-20 03:00:00 | 1 | 0 | 1 | 1 | 10.66 | 12.880 | 56 | 11.0014 |
| 4 | 2011-01-20 04:00:00 | 1 | 0 | 1 | 1 | 10.66 | 12.880 | 56 | 11.0014 |
# Same thing as train and test dataset
submission = pd.read_csv('sampleSubmission.csv')
submission.head()
| datetime | count | |
|---|---|---|
| 0 | 2011-01-20 00:00:00 | 0 |
| 1 | 2011-01-20 01:00:00 | 0 |
| 2 | 2011-01-20 02:00:00 | 0 |
| 3 | 2011-01-20 03:00:00 | 0 |
| 4 | 2011-01-20 04:00:00 | 0 |
Requirements:
count, so it is the label we are setting.casual and registered columns as they are also not present in the test dataset. root_mean_squared_error as the metric to use for evaluation.best_quality to focus on creating the best model.predictor = TabularPredictor(
label="count", problem_type="regression", eval_metric="rmse"
).fit(
train_data=train.drop(['casual', 'registered'], axis=1),
time_limit=600,
presets='best_quality')
No path specified. Models will be saved in: "AutogluonModels/ag-20230611_151853/"
Presets specified: ['best_quality']
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20230611_151853/"
AutoGluon Version: 0.6.2
Python Version: 3.7.10
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Thu May 4 09:55:30 UTC 2023
Train Data Rows: 10886
Train Data Columns: 9
Label Column: count
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 2922.18 MB
Train Data (Original) Memory Usage: 1.52 MB (0.1% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting DatetimeFeatureGenerator...
/usr/local/lib/python3.7/site-packages/autogluon/features/generators/datetime.py:59: FutureWarning: casting datetime64[ns, UTC] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.
good_rows = series[~series.isin(bad_rows)].astype(np.int64)
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']
('object', ['datetime_as_object']) : 1 | ['datetime']
Types of features in processed data (raw dtype, special dtypes):
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['season', 'weather', 'humidity']
('int', ['bool']) : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
0.4s = Fit runtime
9 features in original data used to generate 13 features in processed data.
Train Data (Processed) Memory Usage: 0.98 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.43s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.61s of the 599.56s of remaining time.
-101.5462 = Validation score (-root_mean_squared_error)
0.06s = Training runtime
0.11s = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 395.19s of the 595.14s of remaining time.
-84.1251 = Validation score (-root_mean_squared_error)
0.03s = Training runtime
0.1s = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 394.83s of the 594.78s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-131.4609 = Validation score (-root_mean_squared_error)
66.6s = Training runtime
7.24s = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 318.35s of the 518.3s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-131.0542 = Validation score (-root_mean_squared_error)
29.55s = Training runtime
1.38s = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 284.64s of the 484.6s of remaining time.
-116.5443 = Validation score (-root_mean_squared_error)
10.96s = Training runtime
0.6s = Validation runtime
Fitting model: CatBoost_BAG_L1 ... Training model for up to 270.4s of the 470.35s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-130.5332 = Validation score (-root_mean_squared_error)
200.71s = Training runtime
0.1s = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 65.85s of the 265.8s of remaining time.
-124.5881 = Validation score (-root_mean_squared_error)
4.94s = Training runtime
0.53s = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 57.74s of the 257.69s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-138.2085 = Validation score (-root_mean_squared_error)
72.66s = Training runtime
0.51s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 181.14s of remaining time.
-84.1251 = Validation score (-root_mean_squared_error)
0.64s = Training runtime
0.0s = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 180.41s of the 180.39s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-60.401 = Validation score (-root_mean_squared_error)
53.89s = Training runtime
3.08s = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 121.26s of the 121.23s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-55.1131 = Validation score (-root_mean_squared_error)
25.57s = Training runtime
0.23s = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 91.81s of the 91.79s of remaining time.
-53.407 = Validation score (-root_mean_squared_error)
26.8s = Training runtime
0.61s = Validation runtime
Fitting model: CatBoost_BAG_L2 ... Training model for up to 61.92s of the 61.9s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-55.6347 = Validation score (-root_mean_squared_error)
61.99s = Training runtime
0.05s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -3.93s of remaining time.
-53.0925 = Validation score (-root_mean_squared_error)
0.33s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 604.49s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20230611_151853/")
predictor.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L3 -53.092490 14.530773 554.111094 0.001197 0.330786 3 True 14
1 RandomForestMSE_BAG_L2 -53.407033 11.173833 412.322954 0.608464 26.798874 2 True 12
2 LightGBM_BAG_L2 -55.113121 10.790813 411.092591 0.225444 25.568511 2 True 11
3 CatBoost_BAG_L2 -55.634739 10.619263 447.518704 0.053894 61.994624 2 True 13
4 LightGBMXT_BAG_L2 -60.401023 13.641774 439.418299 3.076405 53.894219 2 True 10
5 KNeighborsDist_BAG_L1 -84.125061 0.104620 0.029401 0.104620 0.029401 1 True 2
6 WeightedEnsemble_L2 -84.125061 0.105885 0.668792 0.001266 0.639390 2 True 9
7 KNeighborsUnif_BAG_L1 -101.546199 0.106041 0.063636 0.106041 0.063636 1 True 1
8 RandomForestMSE_BAG_L1 -116.544294 0.600751 10.962398 0.600751 10.962398 1 True 5
9 ExtraTreesMSE_BAG_L1 -124.588053 0.526679 4.943228 0.526679 4.943228 1 True 7
10 CatBoost_BAG_L1 -130.533194 0.100010 200.710994 0.100010 200.710994 1 True 6
11 LightGBM_BAG_L1 -131.054162 1.375981 29.551827 1.375981 29.551827 1 True 4
12 LightGBMXT_BAG_L1 -131.460909 7.240988 66.602082 7.240988 66.602082 1 True 3
13 NeuralNetFastAI_BAG_L1 -138.208491 0.510299 72.660515 0.510299 72.660515 1 True 8
Number of models trained: 14
Types of models trained:
{'WeightedEnsembleModel', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_LGB', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_CatBoost'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['season', 'weather', 'humidity']
('int', ['bool']) : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
Plot summary of models saved to file: AutogluonModels/ag-20230611_151853/SummaryOfModels.html
*** End of fit() summary ***
{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',
'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'KNeighborsUnif_BAG_L1': -101.54619908446061,
'KNeighborsDist_BAG_L1': -84.12506123181602,
'LightGBMXT_BAG_L1': -131.46090891834504,
'LightGBM_BAG_L1': -131.054161598899,
'RandomForestMSE_BAG_L1': -116.54429428704391,
'CatBoost_BAG_L1': -130.5331939673838,
'ExtraTreesMSE_BAG_L1': -124.58805258915959,
'NeuralNetFastAI_BAG_L1': -138.2084908141198,
'WeightedEnsemble_L2': -84.12506123181602,
'LightGBMXT_BAG_L2': -60.40102314592625,
'LightGBM_BAG_L2': -55.11312122378169,
'RandomForestMSE_BAG_L2': -53.40703329979414,
'CatBoost_BAG_L2': -55.63473865135565,
'WeightedEnsemble_L3': -53.09249015726556},
'model_best': 'WeightedEnsemble_L3',
'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels/ag-20230611_151853/models/KNeighborsUnif_BAG_L1/',
'KNeighborsDist_BAG_L1': 'AutogluonModels/ag-20230611_151853/models/KNeighborsDist_BAG_L1/',
'LightGBMXT_BAG_L1': 'AutogluonModels/ag-20230611_151853/models/LightGBMXT_BAG_L1/',
'LightGBM_BAG_L1': 'AutogluonModels/ag-20230611_151853/models/LightGBM_BAG_L1/',
'RandomForestMSE_BAG_L1': 'AutogluonModels/ag-20230611_151853/models/RandomForestMSE_BAG_L1/',
'CatBoost_BAG_L1': 'AutogluonModels/ag-20230611_151853/models/CatBoost_BAG_L1/',
'ExtraTreesMSE_BAG_L1': 'AutogluonModels/ag-20230611_151853/models/ExtraTreesMSE_BAG_L1/',
'NeuralNetFastAI_BAG_L1': 'AutogluonModels/ag-20230611_151853/models/NeuralNetFastAI_BAG_L1/',
'WeightedEnsemble_L2': 'AutogluonModels/ag-20230611_151853/models/WeightedEnsemble_L2/',
'LightGBMXT_BAG_L2': 'AutogluonModels/ag-20230611_151853/models/LightGBMXT_BAG_L2/',
'LightGBM_BAG_L2': 'AutogluonModels/ag-20230611_151853/models/LightGBM_BAG_L2/',
'RandomForestMSE_BAG_L2': 'AutogluonModels/ag-20230611_151853/models/RandomForestMSE_BAG_L2/',
'CatBoost_BAG_L2': 'AutogluonModels/ag-20230611_151853/models/CatBoost_BAG_L2/',
'WeightedEnsemble_L3': 'AutogluonModels/ag-20230611_151853/models/WeightedEnsemble_L3/'},
'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.06363558769226074,
'KNeighborsDist_BAG_L1': 0.029401063919067383,
'LightGBMXT_BAG_L1': 66.6020815372467,
'LightGBM_BAG_L1': 29.55182695388794,
'RandomForestMSE_BAG_L1': 10.962397575378418,
'CatBoost_BAG_L1': 200.71099424362183,
'ExtraTreesMSE_BAG_L1': 4.943228483200073,
'NeuralNetFastAI_BAG_L1': 72.66051483154297,
'WeightedEnsemble_L2': 0.6393904685974121,
'LightGBMXT_BAG_L2': 53.89421892166138,
'LightGBM_BAG_L2': 25.56851100921631,
'RandomForestMSE_BAG_L2': 26.79887366294861,
'CatBoost_BAG_L2': 61.99462413787842,
'WeightedEnsemble_L3': 0.33078551292419434},
'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.10604143142700195,
'KNeighborsDist_BAG_L1': 0.10461950302124023,
'LightGBMXT_BAG_L1': 7.240987539291382,
'LightGBM_BAG_L1': 1.375981092453003,
'RandomForestMSE_BAG_L1': 0.6007513999938965,
'CatBoost_BAG_L1': 0.10001039505004883,
'ExtraTreesMSE_BAG_L1': 0.5266790390014648,
'NeuralNetFastAI_BAG_L1': 0.5102987289428711,
'WeightedEnsemble_L2': 0.0012657642364501953,
'LightGBMXT_BAG_L2': 3.0764048099517822,
'LightGBM_BAG_L2': 0.22544360160827637,
'RandomForestMSE_BAG_L2': 0.6084635257720947,
'CatBoost_BAG_L2': 0.0538942813873291,
'WeightedEnsemble_L3': 0.0011973381042480469},
'num_bag_folds': 8,
'max_stack_level': 3,
'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'KNeighborsDist_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'LightGBMXT_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'NeuralNetFastAI_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L2': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBMXT_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L3 -53.092490 14.530773 554.111094
1 RandomForestMSE_BAG_L2 -53.407033 11.173833 412.322954
2 LightGBM_BAG_L2 -55.113121 10.790813 411.092591
3 CatBoost_BAG_L2 -55.634739 10.619263 447.518704
4 LightGBMXT_BAG_L2 -60.401023 13.641774 439.418299
5 KNeighborsDist_BAG_L1 -84.125061 0.104620 0.029401
6 WeightedEnsemble_L2 -84.125061 0.105885 0.668792
7 KNeighborsUnif_BAG_L1 -101.546199 0.106041 0.063636
8 RandomForestMSE_BAG_L1 -116.544294 0.600751 10.962398
9 ExtraTreesMSE_BAG_L1 -124.588053 0.526679 4.943228
10 CatBoost_BAG_L1 -130.533194 0.100010 200.710994
11 LightGBM_BAG_L1 -131.054162 1.375981 29.551827
12 LightGBMXT_BAG_L1 -131.460909 7.240988 66.602082
13 NeuralNetFastAI_BAG_L1 -138.208491 0.510299 72.660515
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.001197 0.330786 3 True
1 0.608464 26.798874 2 True
2 0.225444 25.568511 2 True
3 0.053894 61.994624 2 True
4 3.076405 53.894219 2 True
5 0.104620 0.029401 1 True
6 0.001266 0.639390 2 True
7 0.106041 0.063636 1 True
8 0.600751 10.962398 1 True
9 0.526679 4.943228 1 True
10 0.100010 200.710994 1 True
11 1.375981 29.551827 1 True
12 7.240988 66.602082 1 True
13 0.510299 72.660515 1 True
fit_order
0 14
1 12
2 11
3 13
4 10
5 2
6 9
7 1
8 5
9 7
10 6
11 4
12 3
13 8 }
predictions = pd.DataFrame(data={'datetime': test['datetime'], 'Pred_count': predictor.predict(test)})
predictions.head()
| datetime | Pred_count | |
|---|---|---|
| 0 | 2011-01-20 00:00:00 | 24.123066 |
| 1 | 2011-01-20 01:00:00 | 42.460495 |
| 2 | 2011-01-20 02:00:00 | 46.532757 |
| 3 | 2011-01-20 03:00:00 | 50.135544 |
| 4 | 2011-01-20 04:00:00 | 52.505470 |
# Describe the `predictions` series to see if there are any negative values
predictions.describe()
| Pred_count | |
|---|---|
| count | 6493.000000 |
| mean | 100.627243 |
| std | 89.699265 |
| min | 3.202173 |
| 25% | 20.407969 |
| 50% | 64.216888 |
| 75% | 167.488281 |
| max | 364.660767 |
# How many negative values do we have?
def negative_function(value):
return value[value < 0].sum()
negative_values = predictions.groupby(predictions['Pred_count'])
print(negative_values['Pred_count'].agg([('negcount', negative_function)]))
negcount Pred_count 3.202173 0.0 3.227723 0.0 3.231407 0.0 3.265340 0.0 3.364769 0.0 ... ... 362.632050 0.0 363.546387 0.0 364.136047 0.0 364.237183 0.0 364.660767 0.0 [6187 rows x 1 columns]
# Set them to zero
predictions[predictions['Pred_count']<0] = 0
# Check predictions after setting them to zero
predictions.describe()
| Pred_count | |
|---|---|
| count | 6493.000000 |
| mean | 100.627243 |
| std | 89.699265 |
| min | 3.202173 |
| 25% | 20.407969 |
| 50% | 64.216888 |
| 75% | 167.488281 |
| max | 364.660767 |
submission["count"] = predictions['Pred_count']
submission.to_csv("submission.csv", index=False)
!pip install -U kaggle
!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m "first raw submission"
Collecting kaggle
Downloading kaggle-1.5.13.tar.gz (63 kB)
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Preparing metadata (setup.py) ... done
Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/site-packages (from kaggle) (1.16.0)
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Requirement already satisfied: requests in /usr/local/lib/python3.7/site-packages (from kaggle) (2.22.0)
Requirement already satisfied: tqdm in /usr/local/lib/python3.7/site-packages (from kaggle) (4.65.0)
Collecting python-slugify (from kaggle)
Downloading python_slugify-8.0.1-py2.py3-none-any.whl (9.7 kB)
Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/site-packages (from kaggle) (1.25.11)
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Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.7/site-packages (from requests->kaggle) (2.8)
Building wheels for collected packages: kaggle
Building wheel for kaggle (setup.py) ... done
Created wheel for kaggle: filename=kaggle-1.5.13-py3-none-any.whl size=77717 sha256=5db9d2c4a400005ee6a4c1963c2f24603ed18449735ac6319dd763ef590a42a9
Stored in directory: /root/.cache/pip/wheels/fd/97/a6/3372cb23468915cbcf108338dd29c73379fd1a55828ec608ba
Successfully built kaggle
Installing collected packages: python-slugify, kaggle
Successfully installed kaggle-1.5.13 python-slugify-8.0.1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 354kB/s]
Successfully submitted to Bike Sharing Demand
My Submissions¶!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName date description status publicScore privateScore -------------- ------------------- -------------------- -------- ----------- ------------ submission.csv 2023-06-11 15:29:46 first raw submission complete 1.79005 1.79005
1.79005¶# Create a histogram of all features to show the distribution of each one relative to the data. This is part of the exploritory data analysis
train.hist()
array([[<AxesSubplot:title={'center':'season'}>,
<AxesSubplot:title={'center':'holiday'}>,
<AxesSubplot:title={'center':'workingday'}>],
[<AxesSubplot:title={'center':'weather'}>,
<AxesSubplot:title={'center':'temp'}>,
<AxesSubplot:title={'center':'atemp'}>],
[<AxesSubplot:title={'center':'humidity'}>,
<AxesSubplot:title={'center':'windspeed'}>,
<AxesSubplot:title={'center':'casual'}>],
[<AxesSubplot:title={'center':'registered'}>,
<AxesSubplot:title={'center':'count'}>, <AxesSubplot:>]],
dtype=object)
# create a new feature
# Break datetime into year, month, day and hour
train.loc[:, "datetime"] = pd.to_datetime(train.loc[:, "datetime"])
test.loc[:, "datetime"] = pd.to_datetime(test.loc[:, "datetime"])
train['year'] = train['datetime'].dt.year
train['month'] = train['datetime'].dt.month
train['day'] = train['datetime'].dt.day
train['hour'] = train['datetime'].dt.hour
test['year'] = test['datetime'].dt.year
test['month'] = test['datetime'].dt.month
test['day'] = test['datetime'].dt.day
test['hour'] = test['datetime'].dt.hour
train["season"] = train["season"].astype("category")
train["weather"] = train["weather"].astype("category")
test["season"] = test["season"].astype("category")
test["weather"] = test["weather"].astype("category")
# View are new feature
train.head()
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | year | month | day | hour | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0 | 3 | 13 | 16 | 2011 | 1 | 1 | 0 |
| 1 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 8 | 32 | 40 | 2011 | 1 | 1 | 1 |
| 2 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 | 2011 | 1 | 1 | 2 |
| 3 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 3 | 10 | 13 | 2011 | 1 | 1 | 3 |
| 4 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 0 | 1 | 1 | 2011 | 1 | 1 | 4 |
# View histogram of all features again now with the hour feature
train.hist()
array([[<AxesSubplot:title={'center':'datetime'}>,
<AxesSubplot:title={'center':'holiday'}>,
<AxesSubplot:title={'center':'workingday'}>,
<AxesSubplot:title={'center':'temp'}>],
[<AxesSubplot:title={'center':'atemp'}>,
<AxesSubplot:title={'center':'humidity'}>,
<AxesSubplot:title={'center':'windspeed'}>,
<AxesSubplot:title={'center':'casual'}>],
[<AxesSubplot:title={'center':'registered'}>,
<AxesSubplot:title={'center':'count'}>,
<AxesSubplot:title={'center':'year'}>,
<AxesSubplot:title={'center':'month'}>],
[<AxesSubplot:title={'center':'day'}>,
<AxesSubplot:title={'center':'hour'}>, <AxesSubplot:>,
<AxesSubplot:>]], dtype=object)
predictor_new_features = TabularPredictor(
label="count", problem_type="regression", eval_metric="rmse"
).fit(
train_data=train.drop(['casual', 'registered'], axis=1),
time_limit=600,
presets='best_quality')
No path specified. Models will be saved in: "AutogluonModels/ag-20230611_154539/"
Presets specified: ['best_quality']
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20230611_154539/"
AutoGluon Version: 0.6.2
Python Version: 3.7.10
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Thu May 4 09:55:30 UTC 2023
Train Data Rows: 10886
Train Data Columns: 13
Label Column: count
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 1966.6 MB
Train Data (Original) Memory Usage: 0.98 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Fitting DatetimeFeatureGenerator...
/usr/local/lib/python3.7/site-packages/autogluon/features/generators/datetime.py:59: FutureWarning: casting datetime64[ns, UTC] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.
good_rows = series[~series.isin(bad_rows)].astype(np.int64)
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('datetime', []) : 1 | ['datetime']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 7 | ['holiday', 'workingday', 'humidity', 'year', 'month', ...]
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 4 | ['humidity', 'month', 'day', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
0.6s = Fit runtime
13 features in original data used to generate 17 features in processed data.
Train Data (Processed) Memory Usage: 1.1 MB (0.1% of available memory)
Data preprocessing and feature engineering runtime = 0.7s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.43s of the 599.29s of remaining time.
-101.5462 = Validation score (-root_mean_squared_error)
0.04s = Training runtime
0.1s = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 399.07s of the 598.93s of remaining time.
-84.1251 = Validation score (-root_mean_squared_error)
0.03s = Training runtime
0.1s = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 398.7s of the 598.57s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-34.346 = Validation score (-root_mean_squared_error)
86.46s = Training runtime
9.88s = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 307.04s of the 506.9s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.9173 = Validation score (-root_mean_squared_error)
44.2s = Training runtime
3.56s = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 258.08s of the 457.94s of remaining time.
-38.3149 = Validation score (-root_mean_squared_error)
14.47s = Training runtime
0.58s = Validation runtime
Fitting model: CatBoost_BAG_L1 ... Training model for up to 240.51s of the 440.37s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.9718 = Validation score (-root_mean_squared_error)
205.4s = Training runtime
0.17s = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 30.99s of the 230.85s of remaining time.
-38.2952 = Validation score (-root_mean_squared_error)
6.65s = Training runtime
0.56s = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 21.27s of the 221.13s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-103.8428 = Validation score (-root_mean_squared_error)
43.37s = Training runtime
0.49s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 173.84s of remaining time.
-32.1324 = Validation score (-root_mean_squared_error)
0.67s = Training runtime
0.0s = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 173.09s of the 173.06s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-31.1799 = Validation score (-root_mean_squared_error)
33.72s = Training runtime
1.07s = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 134.32s of the 134.29s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-30.4233 = Validation score (-root_mean_squared_error)
25.88s = Training runtime
0.29s = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 104.36s of the 104.33s of remaining time.
-31.5082 = Validation score (-root_mean_squared_error)
31.83s = Training runtime
0.63s = Validation runtime
Fitting model: CatBoost_BAG_L2 ... Training model for up to 69.61s of the 69.58s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-30.3736 = Validation score (-root_mean_squared_error)
67.87s = Training runtime
0.11s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -2.28s of remaining time.
-30.0844 = Validation score (-root_mean_squared_error)
0.29s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 602.77s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20230611_154539/")
predictor_new_features.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L3 -30.084351 16.909217 528.380250 0.000771 0.288950 3 True 14
1 CatBoost_BAG_L2 -30.373557 15.544385 468.489517 0.109781 67.872453 2 True 13
2 LightGBM_BAG_L2 -30.423259 15.729353 426.498674 0.294749 25.881609 2 True 11
3 LightGBMXT_BAG_L2 -31.179943 16.503916 434.337237 1.069312 33.720173 2 True 10
4 RandomForestMSE_BAG_L2 -31.508185 16.060942 432.443680 0.626337 31.826616 2 True 12
5 WeightedEnsemble_L2 -32.132426 14.285585 351.234542 0.001356 0.670159 2 True 9
6 LightGBM_BAG_L1 -33.917339 3.559763 44.200528 3.559763 44.200528 1 True 4
7 CatBoost_BAG_L1 -33.971769 0.170114 205.399064 0.170114 205.399064 1 True 6
8 LightGBMXT_BAG_L1 -34.345997 9.875449 86.456437 9.875449 86.456437 1 True 3
9 ExtraTreesMSE_BAG_L1 -38.295243 0.560143 6.649233 0.560143 6.649233 1 True 7
10 RandomForestMSE_BAG_L1 -38.314947 0.575209 14.474546 0.575209 14.474546 1 True 5
11 KNeighborsDist_BAG_L1 -84.125061 0.103694 0.033808 0.103694 0.033808 1 True 2
12 KNeighborsUnif_BAG_L1 -101.546199 0.103020 0.035200 0.103020 0.035200 1 True 1
13 NeuralNetFastAI_BAG_L1 -103.842847 0.487212 43.368248 0.487212 43.368248 1 True 8
Number of models trained: 14
Types of models trained:
{'WeightedEnsembleModel', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_LGB', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_CatBoost'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 4 | ['humidity', 'month', 'day', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
Plot summary of models saved to file: AutogluonModels/ag-20230611_154539/SummaryOfModels.html
*** End of fit() summary ***
{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',
'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'KNeighborsUnif_BAG_L1': -101.54619908446061,
'KNeighborsDist_BAG_L1': -84.12506123181602,
'LightGBMXT_BAG_L1': -34.34599701170154,
'LightGBM_BAG_L1': -33.91733862651761,
'RandomForestMSE_BAG_L1': -38.31494666065859,
'CatBoost_BAG_L1': -33.97176883234122,
'ExtraTreesMSE_BAG_L1': -38.29524277410234,
'NeuralNetFastAI_BAG_L1': -103.842846933631,
'WeightedEnsemble_L2': -32.13242584358949,
'LightGBMXT_BAG_L2': -31.179942907396665,
'LightGBM_BAG_L2': -30.423258655809637,
'RandomForestMSE_BAG_L2': -31.508184844819915,
'CatBoost_BAG_L2': -30.37355681969245,
'WeightedEnsemble_L3': -30.08435065702291},
'model_best': 'WeightedEnsemble_L3',
'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels/ag-20230611_154539/models/KNeighborsUnif_BAG_L1/',
'KNeighborsDist_BAG_L1': 'AutogluonModels/ag-20230611_154539/models/KNeighborsDist_BAG_L1/',
'LightGBMXT_BAG_L1': 'AutogluonModels/ag-20230611_154539/models/LightGBMXT_BAG_L1/',
'LightGBM_BAG_L1': 'AutogluonModels/ag-20230611_154539/models/LightGBM_BAG_L1/',
'RandomForestMSE_BAG_L1': 'AutogluonModels/ag-20230611_154539/models/RandomForestMSE_BAG_L1/',
'CatBoost_BAG_L1': 'AutogluonModels/ag-20230611_154539/models/CatBoost_BAG_L1/',
'ExtraTreesMSE_BAG_L1': 'AutogluonModels/ag-20230611_154539/models/ExtraTreesMSE_BAG_L1/',
'NeuralNetFastAI_BAG_L1': 'AutogluonModels/ag-20230611_154539/models/NeuralNetFastAI_BAG_L1/',
'WeightedEnsemble_L2': 'AutogluonModels/ag-20230611_154539/models/WeightedEnsemble_L2/',
'LightGBMXT_BAG_L2': 'AutogluonModels/ag-20230611_154539/models/LightGBMXT_BAG_L2/',
'LightGBM_BAG_L2': 'AutogluonModels/ag-20230611_154539/models/LightGBM_BAG_L2/',
'RandomForestMSE_BAG_L2': 'AutogluonModels/ag-20230611_154539/models/RandomForestMSE_BAG_L2/',
'CatBoost_BAG_L2': 'AutogluonModels/ag-20230611_154539/models/CatBoost_BAG_L2/',
'WeightedEnsemble_L3': 'AutogluonModels/ag-20230611_154539/models/WeightedEnsemble_L3/'},
'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.03520035743713379,
'KNeighborsDist_BAG_L1': 0.033808231353759766,
'LightGBMXT_BAG_L1': 86.4564368724823,
'LightGBM_BAG_L1': 44.200528144836426,
'RandomForestMSE_BAG_L1': 14.47454571723938,
'CatBoost_BAG_L1': 205.39906406402588,
'ExtraTreesMSE_BAG_L1': 6.649233102798462,
'NeuralNetFastAI_BAG_L1': 43.368247747421265,
'WeightedEnsemble_L2': 0.6701593399047852,
'LightGBMXT_BAG_L2': 33.7201726436615,
'LightGBM_BAG_L2': 25.881609439849854,
'RandomForestMSE_BAG_L2': 31.826615810394287,
'CatBoost_BAG_L2': 67.87245321273804,
'WeightedEnsemble_L3': 0.28894996643066406},
'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.10301971435546875,
'KNeighborsDist_BAG_L1': 0.10369372367858887,
'LightGBMXT_BAG_L1': 9.875449180603027,
'LightGBM_BAG_L1': 3.5597634315490723,
'RandomForestMSE_BAG_L1': 0.5752091407775879,
'CatBoost_BAG_L1': 0.17011356353759766,
'ExtraTreesMSE_BAG_L1': 0.560143232345581,
'NeuralNetFastAI_BAG_L1': 0.4872124195098877,
'WeightedEnsemble_L2': 0.0013561248779296875,
'LightGBMXT_BAG_L2': 1.0693118572235107,
'LightGBM_BAG_L2': 0.29474902153015137,
'RandomForestMSE_BAG_L2': 0.6263372898101807,
'CatBoost_BAG_L2': 0.10978102684020996,
'WeightedEnsemble_L3': 0.0007710456848144531},
'num_bag_folds': 8,
'max_stack_level': 3,
'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'KNeighborsDist_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'LightGBMXT_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'NeuralNetFastAI_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L2': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBMXT_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L3 -30.084351 16.909217 528.380250
1 CatBoost_BAG_L2 -30.373557 15.544385 468.489517
2 LightGBM_BAG_L2 -30.423259 15.729353 426.498674
3 LightGBMXT_BAG_L2 -31.179943 16.503916 434.337237
4 RandomForestMSE_BAG_L2 -31.508185 16.060942 432.443680
5 WeightedEnsemble_L2 -32.132426 14.285585 351.234542
6 LightGBM_BAG_L1 -33.917339 3.559763 44.200528
7 CatBoost_BAG_L1 -33.971769 0.170114 205.399064
8 LightGBMXT_BAG_L1 -34.345997 9.875449 86.456437
9 ExtraTreesMSE_BAG_L1 -38.295243 0.560143 6.649233
10 RandomForestMSE_BAG_L1 -38.314947 0.575209 14.474546
11 KNeighborsDist_BAG_L1 -84.125061 0.103694 0.033808
12 KNeighborsUnif_BAG_L1 -101.546199 0.103020 0.035200
13 NeuralNetFastAI_BAG_L1 -103.842847 0.487212 43.368248
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.000771 0.288950 3 True
1 0.109781 67.872453 2 True
2 0.294749 25.881609 2 True
3 1.069312 33.720173 2 True
4 0.626337 31.826616 2 True
5 0.001356 0.670159 2 True
6 3.559763 44.200528 1 True
7 0.170114 205.399064 1 True
8 9.875449 86.456437 1 True
9 0.560143 6.649233 1 True
10 0.575209 14.474546 1 True
11 0.103694 0.033808 1 True
12 0.103020 0.035200 1 True
13 0.487212 43.368248 1 True
fit_order
0 14
1 13
2 11
3 10
4 12
5 9
6 4
7 6
8 3
9 7
10 5
11 2
12 1
13 8 }
# Use the new model to prefict using the test data
predictions_new_features = predictor_new_features.predict(test)
predictions_new_features = {'datetime': test['datetime'], 'Pred_count': predictions_new_features}
predictions_new_features = pd.DataFrame(data=predictions_new_features)
# Check the predictions head
predictions_new_features.head()
| datetime | Pred_count | |
|---|---|---|
| 0 | 2011-01-20 00:00:00 | 15.859232 |
| 1 | 2011-01-20 01:00:00 | 11.959195 |
| 2 | 2011-01-20 02:00:00 | 10.928431 |
| 3 | 2011-01-20 03:00:00 | 9.760633 |
| 4 | 2011-01-20 04:00:00 | 8.368550 |
# Remember to set all negative values to zero
predictions_new_features[predictions_new_features['Pred_count']<0] = 0
# Same submitting predictions
submission_new_features = pd.read_csv('submission.csv')
submission_new_features["count"] = predictions_new_features['Pred_count']
submission_new_features.to_csv("submission_new_features.csv", index=False)
!kaggle competitions submit -c bike-sharing-demand -f submission_new_features.csv -m "new features"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 368kB/s] Successfully submitted to Bike Sharing Demand
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName date description status publicScore privateScore --------------------------- ------------------- -------------------- -------- ----------- ------------ submission_new_features.csv 2023-06-11 15:56:21 new features complete 0.68994 0.68994 submission.csv 2023-06-11 15:29:46 first raw submission complete 1.79005 1.79005
0.68994¶hyperparameter and hyperparameter_tune_kwargs arguments.import autogluon.core as ag
# NN Dropout probability
nn_options = {
'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1),
}
# No. of boosting rounds and leaves in trees
gbm_options = {
'num_boost_round': 100,
'num_leaves': ag.space.Int(lower=26, upper=66, default=36),
}
hyperparameters = { # hyperparameters of each model type
'GBM': gbm_options,
'NN': nn_options,
}
# Use at most 3 different hyperparameter configurations for each type of model
num_trials = 3
# Use Bayesian optimization routine with a local scheduler
search_strategy = 'auto'
hyperparameter_tune_kwargs = {
'num_trials': num_trials,
'scheduler' : 'local',
'searcher': search_strategy,
}
predictor_new_hpo = TabularPredictor(
label="count", problem_type="regression", eval_metric="rmse"
).fit(
train_data=train.drop(['casual', 'registered'], axis=1),
time_limit=600,
presets='best_quality', hyperparameters=hyperparameters, hyperparameter_tune_kwargs=hyperparameter_tune_kwargs)
No path specified. Models will be saved in: "AutogluonModels/ag-20230611_155623/"
Presets specified: ['best_quality']
Warning: hyperparameter tuning is currently experimental and may cause the process to hang.
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20230611_155623/"
AutoGluon Version: 0.6.2
Python Version: 3.7.10
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Thu May 4 09:55:30 UTC 2023
Train Data Rows: 10886
Train Data Columns: 13
Label Column: count
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 1990.23 MB
Train Data (Original) Memory Usage: 0.98 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Fitting DatetimeFeatureGenerator...
/usr/local/lib/python3.7/site-packages/autogluon/features/generators/datetime.py:59: FutureWarning: casting datetime64[ns, UTC] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.
good_rows = series[~series.isin(bad_rows)].astype(np.int64)
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('datetime', []) : 1 | ['datetime']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 7 | ['holiday', 'workingday', 'humidity', 'year', 'month', ...]
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 4 | ['humidity', 'month', 'day', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
0.2s = Fit runtime
13 features in original data used to generate 17 features in processed data.
Train Data (Processed) Memory Usage: 1.1 MB (0.1% of available memory)
Data preprocessing and feature engineering runtime = 0.2s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
AutoGluon will fit 2 stack levels (L1 to L2) ...
WARNING: "NN" model has been deprecated in v0.4.0 and renamed to "NN_MXNET". Starting in v0.6.0, specifying "NN" or "NN_MXNET" will raise an exception. Consider instead specifying "NN_TORCH".
Fitting 2 L1 models ...
Hyperparameter tuning model: LightGBM_BAG_L1 ... Tuning model for up to 179.9s of the 599.8s of remaining time.
0%| | 0/3 [00:00<?, ?it/s] Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
33%|███▎ | 1/3 [00:22<00:44, 22.24s/it] Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
67%|██████▋ | 2/3 [00:45<00:22, 22.92s/it] Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
100%|██████████| 3/3 [01:08<00:00, 22.95s/it]
Fitted model: LightGBM_BAG_L1/T1 ...
-40.2554 = Validation score (-root_mean_squared_error)
22.2s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T2 ...
-38.8609 = Validation score (-root_mean_squared_error)
23.37s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1/T3 ...
-38.5604 = Validation score (-root_mean_squared_error)
23.18s = Training runtime
0.0s = Validation runtime
Hyperparameter tuning model: NeuralNetMXNet_BAG_L1 ... Tuning model for up to 179.9s of the 530.81s of remaining time.
0%| | 0/3 [00:00<?, ?it/s] Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=4939, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
time_limit=time_limit,
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
model.fit(**fit_args, time_limit=time_left)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
fold_fitting_strategy.after_all_folds_scheduled()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
raise processed_exception
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=4939, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
33%|███▎ | 1/3 [00:06<00:12, 6.06s/it]2023-06-11 15:57:39,386 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=5000, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
time_limit=time_limit,
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
model.fit(**fit_args, time_limit=time_left)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
fold_fitting_strategy.after_all_folds_scheduled()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
raise processed_exception
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=5000, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
67%|██████▋ | 2/3 [00:11<00:05, 5.70s/it]2023-06-11 15:57:44,832 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=5066, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
time_limit=time_limit,
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
model.fit(**fit_args, time_limit=time_left)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
fold_fitting_strategy.after_all_folds_scheduled()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
raise processed_exception
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=5066, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
100%|██████████| 3/3 [00:16<00:00, 5.63s/it]
No model was trained during hyperparameter tuning NeuralNetMXNet_BAG_L1... Skipping this model.
Repeating k-fold bagging: 2/20
Fitting model: LightGBM_BAG_L1/T1 ... Training model for up to 313.78s of the 513.8s of remaining time.
2023-06-11 15:57:50,187 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-39.7819 = Validation score (-root_mean_squared_error)
41.55s = Training runtime
0.15s = Validation runtime
Fitting model: LightGBM_BAG_L1/T2 ... Training model for up to 290.65s of the 490.67s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-38.487 = Validation score (-root_mean_squared_error)
41.9s = Training runtime
0.16s = Validation runtime
Fitting model: LightGBM_BAG_L1/T3 ... Training model for up to 268.0s of the 468.03s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-38.1028 = Validation score (-root_mean_squared_error)
42.16s = Training runtime
0.16s = Validation runtime
Repeating k-fold bagging: 3/20
Fitting model: LightGBM_BAG_L1/T1 ... Training model for up to 244.81s of the 444.83s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-39.6161 = Validation score (-root_mean_squared_error)
60.32s = Training runtime
0.3s = Validation runtime
Fitting model: LightGBM_BAG_L1/T2 ... Training model for up to 221.23s of the 421.26s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-38.3259 = Validation score (-root_mean_squared_error)
60.57s = Training runtime
0.31s = Validation runtime
Fitting model: LightGBM_BAG_L1/T3 ... Training model for up to 198.37s of the 398.39s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-37.9204 = Validation score (-root_mean_squared_error)
65.43s = Training runtime
0.37s = Validation runtime
Repeating k-fold bagging: 4/20
Fitting model: LightGBM_BAG_L1/T1 ... Training model for up to 170.92s of the 370.95s of remaining time.
Fitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy
-39.5522 = Validation score (-root_mean_squared_error)
79.42s = Training runtime
0.44s = Validation runtime
Fitting model: LightGBM_BAG_L1/T2 ... Training model for up to 147.54s of the 347.57s of remaining time.
Fitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy
-38.2599 = Validation score (-root_mean_squared_error)
78.83s = Training runtime
0.45s = Validation runtime
Fitting model: LightGBM_BAG_L1/T3 ... Training model for up to 124.96s of the 324.99s of remaining time.
Fitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy
-37.8472 = Validation score (-root_mean_squared_error)
85.13s = Training runtime
0.54s = Validation runtime
Repeating k-fold bagging: 5/20
Fitting model: LightGBM_BAG_L1/T1 ... Training model for up to 100.62s of the 300.64s of remaining time.
Fitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy
-39.5237 = Validation score (-root_mean_squared_error)
98.04s = Training runtime
0.59s = Validation runtime
Fitting model: LightGBM_BAG_L1/T2 ... Training model for up to 77.78s of the 277.81s of remaining time.
Fitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy
-38.2323 = Validation score (-root_mean_squared_error)
98.17s = Training runtime
0.64s = Validation runtime
Fitting model: LightGBM_BAG_L1/T3 ... Training model for up to 54.19s of the 254.21s of remaining time.
Fitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy
-37.8113 = Validation score (-root_mean_squared_error)
104.41s = Training runtime
0.72s = Validation runtime
Completed 5/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 230.21s of remaining time.
-37.5326 = Validation score (-root_mean_squared_error)
0.24s = Training runtime
0.0s = Validation runtime
WARNING: "NN" model has been deprecated in v0.4.0 and renamed to "NN_MXNET". Starting in v0.6.0, specifying "NN" or "NN_MXNET" will raise an exception. Consider instead specifying "NN_TORCH".
Fitting 2 L2 models ...
Hyperparameter tuning model: LightGBM_BAG_L2 ... Tuning model for up to 103.46s of the 229.89s of remaining time.
0%| | 0/3 [00:00<?, ?it/s] Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
33%|███▎ | 1/3 [00:23<00:46, 23.17s/it] Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
67%|██████▋ | 2/3 [00:47<00:23, 23.66s/it] Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
100%|██████████| 3/3 [01:11<00:00, 23.70s/it]
Fitted model: LightGBM_BAG_L2/T1 ...
-36.5025 = Validation score (-root_mean_squared_error)
23.13s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T2 ...
-36.3887 = Validation score (-root_mean_squared_error)
23.95s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2/T3 ...
-36.6849 = Validation score (-root_mean_squared_error)
23.9s = Training runtime
0.0s = Validation runtime
Hyperparameter tuning model: NeuralNetMXNet_BAG_L2 ... Tuning model for up to 103.46s of the 158.66s of remaining time.
0%| | 0/3 [00:00<?, ?it/s] Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=8714, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
time_limit=time_limit,
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
model.fit(**fit_args, time_limit=time_left)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
fold_fitting_strategy.after_all_folds_scheduled()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
raise processed_exception
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=8714, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
33%|███▎ | 1/3 [00:05<00:11, 5.89s/it] Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
2023-06-11 16:03:51,868 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
ray::_ray_fit() (pid=8775, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
time_limit=time_limit,
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
model.fit(**fit_args, time_limit=time_left)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
fold_fitting_strategy.after_all_folds_scheduled()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
raise processed_exception
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=8775, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
67%|██████▋ | 2/3 [00:11<00:05, 5.97s/it] Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
2023-06-11 16:03:57,764 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
ray::_ray_fit() (pid=8868, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
time_limit=time_limit,
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
model.fit(**fit_args, time_limit=time_left)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
fold_fitting_strategy.after_all_folds_scheduled()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
raise processed_exception
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=8868, ip=169.255.254.2)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
time_limit=time_limit_fold, **resources, **kwargs_fold)
File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
out = self._fit(**kwargs)
File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
try_import_mxnet()
File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
import mxnet as mx
File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
from . import contrib
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
from . import text
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
from . import embedding
File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
from ... import numpy_extension as _mx_npx
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import
File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
from . import image
File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
import cv2
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
bootstrap()
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
py_module = importlib.import_module(module_name)
File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
100%|██████████| 3/3 [00:18<00:00, 6.11s/it]
No model was trained during hyperparameter tuning NeuralNetMXNet_BAG_L2... Skipping this model.
Repeating k-fold bagging: 2/20
Fitting model: LightGBM_BAG_L2/T1 ... Training model for up to 140.23s of the 140.21s of remaining time.
2023-06-11 16:04:03,782 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-36.3886 = Validation score (-root_mean_squared_error)
42.95s = Training runtime
0.14s = Validation runtime
Fitting model: LightGBM_BAG_L2/T2 ... Training model for up to 116.51s of the 116.49s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-36.2505 = Validation score (-root_mean_squared_error)
43.16s = Training runtime
0.14s = Validation runtime
Fitting model: LightGBM_BAG_L2/T3 ... Training model for up to 93.09s of the 93.07s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-36.4611 = Validation score (-root_mean_squared_error)
44.04s = Training runtime
0.24s = Validation runtime
Completed 2/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 68.65s of remaining time.
-36.1696 = Validation score (-root_mean_squared_error)
0.25s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 531.79s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20230611_155623/")
predictor_new_hpo.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L3 -36.169577 2.328148 388.058489 0.000806 0.246125 3 True 8
1 LightGBM_BAG_L2/T2 -36.250479 2.085000 343.776040 0.140926 43.157349 2 True 6
2 LightGBM_BAG_L2/T1 -36.388639 2.084663 343.570397 0.140589 42.951706 2 True 5
3 LightGBM_BAG_L2/T3 -36.461067 2.186416 344.655014 0.242342 44.036323 2 True 7
4 WeightedEnsemble_L2 -37.532609 1.358947 202.816522 0.000916 0.241940 2 True 4
5 LightGBM_BAG_L1/T3 -37.811320 0.722558 104.408426 0.722558 104.408426 1 True 3
6 LightGBM_BAG_L1/T2 -38.232258 0.635473 98.166155 0.635473 98.166155 1 True 2
7 LightGBM_BAG_L1/T1 -39.523687 0.586043 98.044110 0.586043 98.044110 1 True 1
Number of models trained: 8
Types of models trained:
{'WeightedEnsembleModel', 'StackerEnsembleModel_LGB'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 4 | ['humidity', 'month', 'day', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
Plot summary of models saved to file: AutogluonModels/ag-20230611_155623/SummaryOfModels.html
*** End of fit() summary ***
{'model_types': {'LightGBM_BAG_L1/T1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1/T3': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBM_BAG_L2/T1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2/T3': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'LightGBM_BAG_L1/T1': -39.52368745300766,
'LightGBM_BAG_L1/T2': -38.232258025837844,
'LightGBM_BAG_L1/T3': -37.81131960847469,
'WeightedEnsemble_L2': -37.53260915677494,
'LightGBM_BAG_L2/T1': -36.38863903351911,
'LightGBM_BAG_L2/T2': -36.25047854854333,
'LightGBM_BAG_L2/T3': -36.46106727870545,
'WeightedEnsemble_L3': -36.16957696672271},
'model_best': 'WeightedEnsemble_L3',
'model_paths': {'LightGBM_BAG_L1/T1': '/root/nd009t-c1-intro-to-ml-templates/cd0385-project-starter/project/AutogluonModels/ag-20230611_155623/models/LightGBM_BAG_L1/T1/',
'LightGBM_BAG_L1/T2': '/root/nd009t-c1-intro-to-ml-templates/cd0385-project-starter/project/AutogluonModels/ag-20230611_155623/models/LightGBM_BAG_L1/T2/',
'LightGBM_BAG_L1/T3': '/root/nd009t-c1-intro-to-ml-templates/cd0385-project-starter/project/AutogluonModels/ag-20230611_155623/models/LightGBM_BAG_L1/T3/',
'WeightedEnsemble_L2': 'AutogluonModels/ag-20230611_155623/models/WeightedEnsemble_L2/',
'LightGBM_BAG_L2/T1': '/root/nd009t-c1-intro-to-ml-templates/cd0385-project-starter/project/AutogluonModels/ag-20230611_155623/models/LightGBM_BAG_L2/T1/',
'LightGBM_BAG_L2/T2': '/root/nd009t-c1-intro-to-ml-templates/cd0385-project-starter/project/AutogluonModels/ag-20230611_155623/models/LightGBM_BAG_L2/T2/',
'LightGBM_BAG_L2/T3': '/root/nd009t-c1-intro-to-ml-templates/cd0385-project-starter/project/AutogluonModels/ag-20230611_155623/models/LightGBM_BAG_L2/T3/',
'WeightedEnsemble_L3': 'AutogluonModels/ag-20230611_155623/models/WeightedEnsemble_L3/'},
'model_fit_times': {'LightGBM_BAG_L1/T1': 98.04410982131958,
'LightGBM_BAG_L1/T2': 98.16615533828735,
'LightGBM_BAG_L1/T3': 104.40842580795288,
'WeightedEnsemble_L2': 0.24194049835205078,
'LightGBM_BAG_L2/T1': 42.951706409454346,
'LightGBM_BAG_L2/T2': 43.15734934806824,
'LightGBM_BAG_L2/T3': 44.0363233089447,
'WeightedEnsemble_L3': 0.2461252212524414},
'model_pred_times': {'LightGBM_BAG_L1/T1': 0.586043119430542,
'LightGBM_BAG_L1/T2': 0.6354734897613525,
'LightGBM_BAG_L1/T3': 0.722557544708252,
'WeightedEnsemble_L2': 0.00091552734375,
'LightGBM_BAG_L2/T1': 0.14058923721313477,
'LightGBM_BAG_L2/T2': 0.14092612266540527,
'LightGBM_BAG_L2/T3': 0.2423417568206787,
'WeightedEnsemble_L3': 0.0008058547973632812},
'num_bag_folds': 8,
'max_stack_level': 3,
'model_hyperparams': {'LightGBM_BAG_L1/T1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1/T2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1/T3': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L2': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2/T3': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L3 -36.169577 2.328148 388.058489
1 LightGBM_BAG_L2/T2 -36.250479 2.085000 343.776040
2 LightGBM_BAG_L2/T1 -36.388639 2.084663 343.570397
3 LightGBM_BAG_L2/T3 -36.461067 2.186416 344.655014
4 WeightedEnsemble_L2 -37.532609 1.358947 202.816522
5 LightGBM_BAG_L1/T3 -37.811320 0.722558 104.408426
6 LightGBM_BAG_L1/T2 -38.232258 0.635473 98.166155
7 LightGBM_BAG_L1/T1 -39.523687 0.586043 98.044110
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.000806 0.246125 3 True
1 0.140926 43.157349 2 True
2 0.140589 42.951706 2 True
3 0.242342 44.036323 2 True
4 0.000916 0.241940 2 True
5 0.722558 104.408426 1 True
6 0.635473 98.166155 1 True
7 0.586043 98.044110 1 True
fit_order
0 8
1 6
2 5
3 7
4 4
5 3
6 2
7 1 }
# Use the new predictor to make a prediction
prediction_new_hpo = predictor_new_hpo.predict(test)
prediction_new_hpo = {'datetime': test['datetime'], 'Pred_count': prediction_new_hpo}
prediction_new_hpo = pd.DataFrame(data=prediction_new_hpo)
prediction_new_hpo.head()
| datetime | Pred_count | |
|---|---|---|
| 0 | 2011-01-20 00:00:00 | 10.239550 |
| 1 | 2011-01-20 01:00:00 | 6.499575 |
| 2 | 2011-01-20 02:00:00 | 6.490079 |
| 3 | 2011-01-20 03:00:00 | 6.275881 |
| 4 | 2011-01-20 04:00:00 | 6.275962 |
# Remember to set all negative values to zero
prediction_new_hpo[prediction_new_hpo['Pred_count']<0] = 0
# Same submitting predictions
submission_new_hpo = pd.read_csv('submission.csv')
submission_new_hpo["count"] = prediction_new_hpo['Pred_count']
submission_new_hpo.to_csv("submission_new_hpo.csv", index=False)
!kaggle competitions submit -c bike-sharing-demand -f submission_new_hpo.csv -m "new features with hyperparameters"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 380kB/s] Successfully submitted to Bike Sharing Demand
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName date description status publicScore privateScore --------------------------- ------------------- --------------------------------- -------- ----------- ------------ submission_new_hpo.csv 2023-06-11 16:06:29 new features with hyperparameters complete 0.48752 0.48752 submission_new_features.csv 2023-06-11 15:56:21 new features complete 0.68994 0.68994 submission.csv 2023-06-11 15:29:46 first raw submission complete 1.79005 1.79005
0.48752¶# Taking the top model score from each training run and creating a line plot to show improvement
# You can create these in the notebook and save them to PNG or use some other tool (e.g. google sheets, excel)
fig = pd.DataFrame(
{
"model": ["initial", "add_features", "hpo"],
"score": [53.092490, 30.084351, 36.169577]
}
).plot(x="model", y="score", figsize=(8, 6)).get_figure()
fig.savefig('model_train_score.png')
# Take the 3 kaggle scores and creating a line plot to show improvement
fig = pd.DataFrame(
{
"test_eval": ["initial", "add_features", "hpo"],
"score": [1.79005, 0.68994, 0.48752]
}
).plot(x="test_eval", y="score", figsize=(8, 6)).get_figure()
fig.savefig('model_test_score.png')
# The 3 hyperparameters we tuned with the kaggle score as the result
hyperparams_df = pd.DataFrame({
"model": ["initial_model", "add_features_model", "hpo_model"],
"hpo1": ['default_vals', 'default_vals', 'GBM: num_leaves: lower=26, upper=66'],
"hpo2": ['default_vals', 'default_vals', 'NN: dropout_prob: 0.0, 0.5'],
"hpo3": ['default_vals', 'default_vals', 'GBM: num_boost_round: 100'],
"score": [1.79005, 0.68994, 0.48752]
})
hyperparams_df.head()
| model | hpo1 | hpo2 | hpo3 | score | |
|---|---|---|---|---|---|
| 0 | initial_model | default_vals | default_vals | default_vals | 1.79005 |
| 1 | add_features_model | default_vals | default_vals | default_vals | 0.68994 |
| 2 | hpo_model | GBM: num_leaves: lower=26, upper=66 | NN: dropout_prob: 0.0, 0.5 | GBM: num_boost_round: 100 | 0.48752 |
def plot_series(time, series, format="-", start=0, end=None, label=None):
plt.plot(time[start:end], series[start:end], format, label=label)
plt.xlabel("Time")
plt.ylabel("Value")
if label:
plt.legend(fontsize=14)
plt.grid(True)
sub_new_features = pd.read_csv('submission_new_features.csv')
# Plotting time series of testing data
import matplotlib.pyplot as plt
series = train["count"].to_numpy()
time = train["datetime"].to_numpy()
plt.figure(figsize=(350, 15))
plot_series(time, series)
plt.title("Training data time series graph")
plt.show()
# Plotting time series of testing data
sub_new_features.loc[:, "datetime"] = pd.to_datetime(sub_new_features.loc[:, "datetime"])
series1 = sub_new_features["count"].to_numpy()
time1 = sub_new_features["datetime"].to_numpy()
plt.figure(figsize=(350, 15))
plot_series(time1, series1)
plt.title("Testing data time series graph")
plt.show()